Discriminating DDoS attacks from flash crowds using flow correlation coefficient

Yu, Shui, Zhou, Wanlei, Jia, Weijia, Guo, Song, Xiang, Yong and Tang, Feilong 2012, Discriminating DDoS attacks from flash crowds using flow correlation coefficient, IEEE transactions on parallel and distributed systems, vol. 23, no. 6, pp. 1073-1080.

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Title Discriminating DDoS attacks from flash crowds using flow correlation coefficient
Author(s) Yu, Shui
Zhou, Wanlei
Jia, Weijia
Guo, Song
Xiang, Yong
Tang, Feilong
Journal name IEEE transactions on parallel and distributed systems
Volume number 23
Issue number 6
Start page 1073
End page 1080
Total pages 8
Publisher IEEE
Place of publication Piscataway, N. J.
Publication date 2012-04-25
ISSN 1045-9219
1558-2183
Keyword(s) DDoS attacks
flash crowds
similarity
discrimination
Summary Distributed Denial of Service (DDoS) attack is a critical threat to the Internet, and botnets are usually the engines behind them. Sophisticated botmasters attempt to disable detectors by mimicking the traffic patterns of flash crowds. This poses a critical challenge to those who defend against DDoS attacks. In our deep study of the size and organization of current botnets, we found that the current attack flows are usually more similar to each other compared to the flows of flash crowds. Based on this, we proposed a discrimination algorithm using the flow correlation coefficient as a similarity metric among suspicious flows. We formulated the problem, and presented theoretical proofs for the feasibility of the proposed discrimination method in theory. Our extensive experiments confirmed the theoretical analysis and demonstrated the effectiveness of the proposed method in practice.
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2012, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30047007

Document type: Journal Article
Collection: School of Information Technology
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